用大模型动态生成对话槽，让护士长面试更自然高效
A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation
- 基于大模型实时生成对话槽，突破预设槽位限制
- 引入溯因推理，使信息收集更准确且自然
- 适合需要灵活访谈的管理场景，如人事评估
本研究旨在提升护理管理者进行职业面试的效率与质量。为此，我们开发了一种基于槽位填充的对话系统，在正式面试前通过预面试收集员工职业信息。传统槽位填充系统受限于预设槽位，难以灵活应对对话变化。本文提出一种基于大语言模型（LLMs）的方法，根据对话流动态生成新槽位，并引入溯因（abduction）机制以实现更恰当、高效的槽位生成。为验证有效性，我们使用用户模拟器进行了实验。结果表明，结合溯因的动态槽位生成方法显著提升了信息收集能力与对话自然度。
原文摘要 · Abstract (English)
This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. To this end, we have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers as a preparatory step before the actual interviews. Conventional slot-filling-based interview dialogue systems have limitations in the flexibility of information collection because the dialogue progresses based on predefined slot sets. We therefore propose a method that leverages large language models (LLMs) to dynamically generate new slots according to the flow of the dialogue, achieving more natural conversations. Furthermore, we incorporate abduction into the slot generation process to enable more appropriate and effective slot generation. To validate the effectiveness of the proposed method, we conducted experiments using a user simulator. The results suggest that the proposed method using abduction is effective in enhancing both information-collecting capabilities and the naturalness of the dialogue.
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